`sparknlp_jsl.annotator.context.contextual_parser`#

Module Contents#

Classes#

`ContextualParserApproach`	Creates a model that extracts entity from a document based on user defined rules.
`ContextualParserModel`	Extracts entity from a document based on user defined rules based on a RegexMatcher.

class ContextualParserApproach#

Bases: sparknlp_jsl.common.AnnotatorApproachInternal, sparknlp_jsl.annotator.HandleExceptionParams

Creates a model that extracts entity from a document based on user defined rules.

Rule matching is based on a RegexMatcher defined in a JSON file that can be set through the method setJsonPath(). In this JSON file, regex is defined that you want to match along with the information that will output on metadata field.

Additionally, a dictionary can be provided with the setDictionary() method to map extracted entities to a unified representation. The first column of the dictionary file should be the representation with following columns the possible matches.

Input Annotation types	Output Annotation type
`DOCUMENT, TOKEN`	`CHUNK`

Parameters:

jsonPath – Path to json file with rules
caseSensitive – Whether to use case sensitive when matching values
prefixAndSuffixMatch – Whether to match both prefix and suffix to annotate the hit
dictionary – Path to dictionary file in tsv or csv format
optionalContextRules – When set to true, it will output regex match regardless of context matches
shortestContextMatch – When set to true, it will stop finding for matches when prefix/suffix data is found in the text.
completeContextMatch – Whether to do an exact match of prefix and suffix.

Examples

>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp_jsl.common import *
>>> from sparknlp.annotator import *
>>> from sparknlp.training import *
>>> import sparknlp_jsl
>>> from sparknlp_jsl.base import *
>>> from sparknlp_jsl.annotator import *
>>> from pyspark.ml import Pipeline

>>> documentAssembler = DocumentAssembler()     ...   .setInputCol("text")     ...   .setOutputCol("document")
...
>>> sentenceDetector = SentenceDetector()     ...   .setInputCols(["document"])     ...   .setOutputCol("sentence")
...
>>> tokenizer = Tokenizer()     ...   .setInputCols(["sentence"])     ...   .setOutputCol("token")

Define the parser (json file needs to be provided)

>>> data = spark.createDataFrame([["A patient has liver metastases pT1bN0M0 and the T5 primary site may be colon or... "]]).toDF("text")
>>> contextualParser = ContextualParserApproach()     ...   .setInputCols(["sentence", "token"])     ...   .setOutputCol("entity")     ...   .setJsonPath("/path/to/regex_token.json")     ...   .setCaseSensitive(True)
...
>>> pipeline = Pipeline(stages=[
...     documentAssembler,
...     sentenceDetector,
...     tokenizer,
...     contextualParser
...   ])

>>> result = pipeline.fit(data).transform(data)
>>> result.selectExpr("explode(entity)").show(5, truncate=False)

col

{chunk, 32, 39, pT1bN0M0, {field -> Stage, normalized -> , confidence -> 1.00, sentence -> 0}, []} {chunk, 49, 50, T5, {field -> Stage, normalized -> , confidence -> 1.00, sentence -> 0}, []} {chunk, 148, 156, cT4bcN2M1, {field -> Stage, normalized -> , confidence -> 1.00, sentence -> 1}, []} {chunk, 189, 194, T?N3M1, {field -> Stage, normalized -> , confidence -> 1.00, sentence -> 2}, []} {chunk, 316, 323, pT1bN0M0, {field -> Stage, normalized -> , confidence -> 1.00, sentence -> 3}, []}

caseSensitive#

completeContextMatch#

dictionary#

doExceptionHandling#

getter_attrs = []#

inputAnnotatorTypes#

inputCols#

jsonPath#

lazyAnnotator#

optionalContextRules#

optionalInputAnnotatorTypes = []#

outputAnnotatorType#

outputCol#

prefixAndSuffixMatch#

shortestContextMatch#

skipLPInputColsValidation = True#

clear(param)#: Clears a param from the param map if it has been explicitly set.

copy(extra=None)#

Creates a copy of this instance with the same uid and some extra params. This implementation first calls Params.copy and then make a copy of the companion Java pipeline component with extra params. So both the Python wrapper and the Java pipeline component get copied.

Parameters:: extra (dict, optional) – Extra parameters to copy to the new instance
Returns:: Copy of this instance
Return type:: JavaParams

explainParam(param)#: Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.

explainParams()#: Returns the documentation of all params with their optionally default values and user-supplied values.

extractParamMap(extra=None)#

Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values < user-supplied values < extra.

Parameters:: extra (dict, optional) – extra param values
Returns:: merged param map
Return type:: dict

fit(dataset, params=None)#

Fits a model to the input dataset with optional parameters.

New in version 1.3.0.

Parameters:

dataset (pyspark.sql.DataFrame) – input dataset.
params (dict or list or tuple, optional) – an optional param map that overrides embedded params. If a list/tuple of param maps is given, this calls fit on each param map and returns a list of models.

Returns:

fitted model(s)

Return type:

Transformer or a list of Transformer

fitMultiple(dataset, paramMaps)#

Fits a model to the input dataset for each param map in paramMaps.

New in version 2.3.0.

Parameters:

dataset (pyspark.sql.DataFrame) – input dataset.
paramMaps (collections.abc.Sequence) – A Sequence of param maps.

Returns:

A thread safe iterable which contains one model for each param map. Each call to next(modelIterator) will return (index, model) where model was fit using paramMaps[index]. index values may not be sequential.

Return type:

_FitMultipleIterator

getInputCols()#: Gets current column names of input annotations.

getLazyAnnotator()#: Gets whether Annotator should be evaluated lazily in a RecursivePipeline.

getOrDefault(param)#: Gets the value of a param in the user-supplied param map or its default value. Raises an error if neither is set.

getOutputCol()#: Gets output column name of annotations.

getParam(paramName)#: Gets a param by its name.

getParamValue(paramName)#

Gets the value of a parameter.

Parameters:: paramName (str) – Name of the parameter

hasDefault(param)#: Checks whether a param has a default value.

hasParam(paramName)#: Tests whether this instance contains a param with a given (string) name.

inputColsValidation(value)#

isDefined(param)#: Checks whether a param is explicitly set by user or has a default value.

isSet(param)#: Checks whether a param is explicitly set by user.

classmethod load(path)#: Reads an ML instance from the input path, a shortcut of read().load(path).

classmethod read()#: Returns an MLReader instance for this class.

save(path)#: Save this ML instance to the given path, a shortcut of ‘write().save(path)’.

set(param, value)#: Sets a parameter in the embedded param map.

setCaseSensitive(value)#

Sets whether to use case sensitive when matching values

Parameters:: value (bool) – Whether to use case sensitive when matching values

setCompleteContextMatch(value)#

Sets whether to do an exact match of prefix and suffix.

Parameters:: value (bool) – When set to true, it will make an exact match, i.e. regex with boundaries

setDictionary(path, read_as=ReadAs.TEXT, options=None)#

Sets dictionary. If set, it replaces regex from JSON config file”

Parameters:

path (str) – Path for dictionary location
read_as (ReadAs) – Format of the file
options (dict) – Dictionary with the options to read the file.

setDoExceptionHandling(value: bool)#

If True, exceptions are handled. If exception causing data is passed to the model, a error annotation is emitted which has the exception message. Processing continues with the next one. This comes with a performance penalty.

Parameters:: value (bool) – If True, exceptions are handled.

setForceInputTypeValidation(etfm)#

setInputCols(*value)#

Sets column names of input annotations.

Parameters:: *value (List[str]) – Input columns for the annotator

setJsonPath(value)#

Sets path to json file with rules

Parameters:: value (str) – Path to json file with rules

setLazyAnnotator(value)#

Sets whether Annotator should be evaluated lazily in a RecursivePipeline.

Parameters:: value (bool) – Whether Annotator should be evaluated lazily in a RecursivePipeline

setOptionalContextRules(value)#

Sets whether it will output regex match regardless of context matches.

Parameters:: value (bool) – When set to true, it will output regex match regardless of context matches.

setOutputCol(value)#

Sets output column name of annotations.

Parameters:: value (str) – Name of output column

setParamValue(paramName)#

Sets the value of a parameter.

Parameters:: paramName (str) – Name of the parameter

setPrefixAndSuffixMatch(value)#

Sets whether to match both prefix and suffix to annotate the hit

Parameters:: value (bool) – Whether to match both prefix and suffix to annotate the hit

setShortestContextMatch(value)#

Sets whether to stop finding for matches when prefix/suffix data is found in the text.

Parameters:: value (bool) – When set to true, it will stop finding for matches when prefix/suffix data is found in the text.

write()#: Returns an MLWriter instance for this ML instance.

class ContextualParserModel(classname='com.johnsnowlabs.nlp.annotators.context.ContextualParserModel', java_model=None)#

Bases: sparknlp_jsl.common.AnnotatorModelInternal, sparknlp_jsl.annotator.HandleExceptionParams

Extracts entity from a document based on user defined rules based on a RegexMatcher.

To train a custom model, check the documentation of ContextualParserApproach.

Input Annotation types	Output Annotation type
`DOCUMENT, TOKEN`	`CHUNK`

Parameters:

caseSensitive – Whether to use case sensitive when matching values
prefixAndSuffixMatch – Whether to match both prefix and suffix to annotate the hit
optionalContextRules – When set to true, it will output regex match regardless of context matches
shortestContextMatch – When set to true, it will stop finding for matches when prefix/suffix data is found in the text.

Examples

>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp_jsl.common import *
>>> from sparknlp.annotator import *
>>> from sparknlp.training import *
>>> import sparknlp_jsl
>>> from sparknlp_jsl.base import *
>>> from sparknlp_jsl.annotator import *
>>> from pyspark.ml import Pipeline

Which means to extract the stage code on a sentence level. An example pipeline could then be defined like this Pipeline could then be defined like this

>>> documentAssembler = DocumentAssembler()     ...   .setInputCol("text")     ...   .setOutputCol("document")
...
>>> sentenceDetector = SentenceDetector()     ...   .setInputCols(["document"])     ...   .setOutputCol("sentence")
...
>>> tokenizer = Tokenizer()     ...   .setInputCols(["sentence"])     ...   .setOutputCol("token")

>>> data = spark.createDataFrame([["A patient has liver metastases pT1bN0M0 and the T5 primary site may be colon or... "]]).toDF("text")
>>> contextualParser = ContextualParserModel.load("mycontextualParserModel")     ...   .setInputCols(["sentence", "token"])     ...   .setOutputCol("entity")     ...
>>> pipeline = Pipeline(stages=[
...     documentAssembler,
...     sentenceDetector,
...     tokenizer,
...     contextualParser
...   ])

>>> result = pipeline.fit(data).transform(data)
>>> result.selectExpr("explode(entity)").show(5, truncate=False)

col

caseSensitive#

doExceptionHandling#

getter_attrs = []#

inputAnnotatorTypes#

inputCols#

lazyAnnotator#

name = ContextualParserModel#

optionalContextRules#

optionalInputAnnotatorTypes = []#

outputAnnotatorType#

outputCol#

prefixAndSuffixMatch#

shortestContextMatch#

skipLPInputColsValidation = True#

clear(param)#: Clears a param from the param map if it has been explicitly set.

copy(extra=None)#

Parameters:: extra (dict, optional) – Extra parameters to copy to the new instance
Returns:: Copy of this instance
Return type:: JavaParams

explainParam(param)#: Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.

explainParams()#: Returns the documentation of all params with their optionally default values and user-supplied values.

extractParamMap(extra=None)#

Parameters:: extra (dict, optional) – extra param values
Returns:: merged param map
Return type:: dict

getInputCols()#: Gets current column names of input annotations.

getLazyAnnotator()#: Gets whether Annotator should be evaluated lazily in a RecursivePipeline.

getOrDefault(param)#: Gets the value of a param in the user-supplied param map or its default value. Raises an error if neither is set.

getOutputCol()#: Gets output column name of annotations.

getParam(paramName)#: Gets a param by its name.

getParamValue(paramName)#

Gets the value of a parameter.

Parameters:: paramName (str) – Name of the parameter

hasDefault(param)#: Checks whether a param has a default value.

hasParam(paramName)#: Tests whether this instance contains a param with a given (string) name.

inputColsValidation(value)#

isDefined(param)#: Checks whether a param is explicitly set by user or has a default value.

isSet(param)#: Checks whether a param is explicitly set by user.

classmethod load(path)#: Reads an ML instance from the input path, a shortcut of read().load(path).

static pretrained(name='date_of_birth_parser', lang='en', remote_loc='clinical/models')#

Download a pre-trained ContextualParserModel.

Parameters:

name (str) – Name of the pre-trained model, by default “date_of_birth_parser”
lang (str) – Language of the pre-trained model, by default “en”
remote_loc (str) – Remote location of the pre-trained model. If None, use the open-source location. Other values are “clinical/models”,”finance/models”, or “legal/models”.
Returns – ContextualParserModel: A pre-trained ContextualParserModel.

classmethod read()#: Returns an MLReader instance for this class.

save(path)#: Save this ML instance to the given path, a shortcut of ‘write().save(path)’.

set(param, value)#: Sets a parameter in the embedded param map.

setCaseSensitive(value)#

Sets whether to use case sensitive when matching values

Parameters:: value (bool) – Whether to use case sensitive when matching values

setDoExceptionHandling(value: bool)#

Parameters:: value (bool) – If True, exceptions are handled.

setForceInputTypeValidation(etfm)#

setInputCols(*value)#

Sets column names of input annotations.

Parameters:: *value (List[str]) – Input columns for the annotator

setLazyAnnotator(value)#

Sets whether Annotator should be evaluated lazily in a RecursivePipeline.

Parameters:: value (bool) – Whether Annotator should be evaluated lazily in a RecursivePipeline

setOptionalContextRules(value)#

Sets whether it will output regex match regardless of context matches.

Parameters:: value (bool) – When set to true, it will output regex match regardless of context matches.

setOutputCol(value)#

Sets output column name of annotations.

Parameters:: value (str) – Name of output column

setParamValue(paramName)#

Sets the value of a parameter.

Parameters:: paramName (str) – Name of the parameter

setParams()#

setPrefixAndSuffixMatch(value)#

Sets whether to match both prefix and suffix to annotate the hit

Parameters:: value (bool) – Whether to match both prefix and suffix to annotate the hit

setShortestContextMatch(value)#

Sets whether to stop finding for matches when prefix/suffix data is found in the text.

Parameters:: value (bool) – When set to true, it will stop finding for matches when prefix/suffix data is found in the text.

transform(dataset, params=None)#

Transforms the input dataset with optional parameters.

New in version 1.3.0.

Parameters:

dataset (pyspark.sql.DataFrame) – input dataset
params (dict, optional) – an optional param map that overrides embedded params.

Returns:

transformed dataset

Return type:

pyspark.sql.DataFrame

write()#: Returns an MLWriter instance for this ML instance.

sparknlp_jsl.annotator.context.contextual_parser#

Module Contents#

Classes#

`sparknlp_jsl.annotator.context.contextual_parser`#